akasha-memory

name	akasha-memory
description	Store, retrieve, and query shared memory records in Akasha — a distributed cognitive fabric for intelligent agent collaboration. Use when the agent needs persistent memory, wants to share findings with other agents, or needs to read context written by other agents. Supports working, episodic, semantic, and procedural memory layers with automatic consolidation via the Nidra engine.
license	ASL-1.0
compatibility	Requires curl or httpx. Works with any LLM agent that can make HTTP requests.
metadata	{"author":"ocuil","version":"1.0.4","category":"memory","tags":"memory shared-memory agents collaboration distributed"}

Akasha Memory Skill

You have access to Akasha, a shared cognitive fabric. Use it to persist and retrieve structured memory across sessions and across agents.

Configuration

Before using this skill, ensure you have:

• AKASHA_URL: The Akasha endpoint (e.g., https://localhost:7777)
• AKASHA_TOKEN: A valid JWT token or API key (e.g., ak_xxx)

Memory Layers

Layer	Path prefix	Purpose	Lifecycle
Working	memory/working/	Active task state, scratchpad	Auto-expires (TTL)
Episodic	memory/episodic/	Events, experiences, interactions	Consolidates via Nidra
Semantic	memory/semantic/	Distilled knowledge, insights	Permanent
Procedural	memory/procedural/	How-to instructions, recipes	Permanent

Operations

Write a record

curl -sk -X POST "$AKASHA_URL/api/v1/records/<namespace>/<path>" \
  -H "Authorization: Bearer $AKASHA_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"value": {"key": "value"}, "ttl_secs": 3600}'

• ttl_secs is optional. Omit for permanent records.
• value can be any valid JSON object.

Read a record

curl -sk "$AKASHA_URL/api/v1/records/<namespace>/<path>" \
  -H "Authorization: Bearer $AKASHA_TOKEN"

Query records (glob pattern)

curl -sk "$AKASHA_URL/api/v1/query?pattern=memory/working/**" \
  -H "Authorization: Bearer $AKASHA_TOKEN"

Delete a record

curl -sk -X DELETE "$AKASHA_URL/api/v1/records/<namespace>/<path>" \
  -H "Authorization: Bearer $AKASHA_TOKEN"

Recommended Path Structure

memory/working/<agent-id>/current-task     → Active task context
memory/working/<agent-id>/scratchpad       → Temporary notes
memory/episodic/<domain>/<event-id>        → What happened  
memory/semantic/<domain>/<insight-id>      → What was learned
memory/procedural/<domain>/<procedure-id>  → How to do things

Best Practices

1. Always namespace by agent-id for working memory to avoid collisions
2. Use semantic memory for important conclusions you want other agents to find
3. Set TTLs on working memory — it's meant to be ephemeral
4. Query before writing — check if similar knowledge already exists
5. Use descriptive paths — they serve as the primary discovery mechanism

Python SDK

If available, use the Python SDK for a cleaner API. See references/python-sdk.md.